code: def sparse_to_dense(w, mask, block, zero=0):
      """Converts sparse matrix to dense matrix with explicit zeros"""
      maskedw = w.clone()
      for bz, wz in enumerate(range(0, w.size(0))):
          for bh, wh in enumerate(range(0, w.size(1))):
              for bi, wi in enumerate(range(0, w.size(2), block)):
                  for bj, wj in enumerate(range(0, w.size(3), block)):
                      if mask[bh, bi, bj] == 0:
                          maskedw[
                              wz, wh, wi : wi + block, wj : wj + block
                          ] = zero
                      # maskedw[wz, wh, wi : wi+block, wj : wj+block] *= mask[bh, bi, bj]
      return maskedw
  
file_path: DeepSpeed/tests/unit/ops/sparse_attention/test_sparse_attention.py
imports: import pytest
  import torch
  import deepspeed
  from deepspeed.accelerator import get_accelerator
  from deepspeed.ops.op_builder import SparseAttnBuilder
  from unit.util import skip_on_arch, skip_on_cuda
label_desc: Use image node_id 2 for calling a global function with example usage: sparse_to_dense(w, mask, block, zero) and returns: maskedw